The user who asked this question is already ahead of most spreadsheet operators. They have Power Query pipelines, VBA macros, and a willingness to "vibe code." That combination alone puts them in the minority of Excel users. But the question they are asking reveals something more important: they sense a ceiling. Power Query is excellent at reshaping tabular data. VBA is fine for automating file operations. Yet neither tool was designed for the kind of flexible, analytical thinking that modern data work demands. Python in Excel is not a replacement for those tools. It is an expansion of what you can reason about inside a spreadsheet.
Consider what Power Query cannot do well. It struggles with statistical modeling, natural language processing, web scraping, and any operation that requires iterating over data with conditional logic that changes per row. VBA can force those behaviors, but the code becomes brittle and hard to maintain. Python brings libraries like pandas for data manipulation, scikit-learn for machine learning, and requests for API calls. The user mentions creating folders and renaming files. Python's pathlib module handles that with fewer lines and fewer edge cases than VBA. More importantly, Python lets you chain operations that would require multiple Power Query steps and interim tables. You can scrape a webpage, parse the HTML, clean the text, run sentiment analysis, and output a classification label, all within one Python cell in Excel. Power Query would need multiple queries, external scripts, or manual intervention to bridge those gaps.
The practical takeaway is this: Python in Excel is for the moments when your data stops being neat rows and starts being messy reality. If you need to pull data from an API that returns JSON with nested dictionaries, Python handles it natively. If you need to apply a regression model to forecast next quarter's numbers, Python's statsmodels library gives you the output in a dataframe you can return directly to the grid. If you need to clean text that contains misspellings, inconsistent capitalization, and emoji, Python's regex and string methods are far more expressive than Power Query's UI-based transformations. The user's "vibe coding" approach suggests they are comfortable experimenting. That is exactly the mindset Python rewards. Write a line, test it, adjust it. No compilation, no separate IDE, no export-import cycle.
The mistake would be to treat Python as a universal upgrade. It is not. Power Query remains faster for standard ETL patterns like merging files from a folder or unpivoting columns. VBA still has the edge for interacting with Excel's object model, like adjusting print settings or protecting sheets. But the user's original question points to the right strategy: keep Power Query for what it does best, and reach for Python when you hit the boundary of what a point-and-click interface can express. That boundary is closer than most people realize. The best next step is to open a cell, type =PY, and try one task that currently frustrates you. Not all of them. Just one. See if the ceiling moves.